Fitting ARIMA model for volatility insurance time series data

The volatility of stock  market data have contributed an essential section in risk study and it is very serious problem especially in emerging markets. Previously it is measured by standard deviation of the return. Therefore, in this article the volatility data will be predicted based on Autoregressive Integrated Moving Average model  (ARIMA) using insurance stock market data from Amman Stock Exchange (ASE) from January 2019 to December 2019. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, These results will be helpful for the investments. Keywords: ARIMA model, forecasting, Insurance Sector DOI: 10.7176/EJBM/11-36-13 Publication date: December 31st 201

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Short Term Predicting Volatility Service Jordanian Sector

Stock market volatility have added an important section in risk scholarship and it is actual  problem particularly in emerging markets. Earlier, it is measured by standard deviation of the  return. Consequently, in this research the volatility data will be predicted based on ARIMA model (Autoregressive Integrated Moving Average model) of the service sector  in Amman Stock Exchange (ASE) from January 2019 to December 2019. Consequently this article shows that the ARIMA model has important results in prediction. Therefore, These outcomes will be helpful for the investments. Keywords: ARIMA model, forecasting, Service sector. DOI: 10.7176/EJBM/11-36-14 Publication date: December 31st 201

Model Peramalan Inflasi Bahan Makanan Primer dengan Pendekatan Box-Jenkins: Studi kasus di Kota Palu

Inflasi merupakan naiknya harga barang dan jasa secara umum dan berkelanjutan pada periode tertentu. Inflasi pada umumnya disebabkan oleh peningkatan permintaan agregat, kenaikan biaya produksi serta perkiraan nilai inflasi pada masa yang akan datang. Inflasi merupakan data deret waktu yang sulit diprediksi karena mengandung komponen tren, musiman, siklus, dan acak. Penelitian ini bertujuan untuk mencari model terbaik inflasi bahan makanan primer, menggunakan model ARIMA. Model terbaik yang diperoleh berdasarkan subkelompok bahan makanan adalah padi-padian, umbi-umbian, dan hasil-hasilnya (25,0,4): daging dan hasil-hasilnya (2,0,10): ikan segar (2,0,8): ikan diawetkan (2,0,8): telur, susu, dan hasil-hasilnya (12,0,20): sayur-sayuran (12,0,12): kacang-kacangan (14,0,13): buah-buahan (8,0,1): bumbu-bumbuan (1,0,1): lemak dan minyak (19,0,0) dan bahan makanan lainnya (25,0,3)

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Three layer wavelet based modeling for river flow

All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data

Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies